SoroushVahidi/scidocs huggingface dataset:
A New Paradigm for Collision-free Hashing: Incrementality at Reduced Cost
(1997)

We present a simple, new paradigm for the design of collision-free hash functions. Any function emanating from this paradigm is incremental. (This means that if a message x which I have previously hashed is modi ed to x0 then rather than having to re-compute the hash of x 0 from scratch, I can quickly \update" the old hash value to the new one, in time proportional to the amount of modi cation made in x to get x.) Also any function emanating from this paradigm is parallelizable, useful for hardware implementation. We derive several speci c functions from our paradigm. All use a standard hash function, assumed ideal, and some algebraic operations. The rst function, MuHASH, uses one modular multiplication per block of the message, making it reasonably e cient, and signi cantly faster than previous incremental hash functions. Its security is proven, based on the hardness of the discrete logarithm problem. A second function, AdHASH, is even faster, using additions instead of multiplications, with security proven given either that approximation of the length of shortest lattice vectors is hard or that the weighted subset sum problem is hard. A third function, LtHASH, is a practical variant of recent lattice based functions, with security proven based, again on the hardness of shortest lattice vector approximation. Dept. of Computer Science & Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. E-Mail: mihir@cs.ucsd.edu. URL: http://www-cse.ucsd.edu/users/mihir. Supported in part by NSF CAREER Award CCR-9624439 and a Packard Foundation Fellowship in Science and Engineering. yMIT Laboratory for Computer Science, 545 Technology Square, Cambridge, MA 02139, USA. E-Mail: miccianc@theory.lcs.mit.edu. Supported in part by DARPA contract DABT63-96-C-0018.
Bayesian Rose Trees
(2010)

Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while computationally convenient, is often undesirable. In this paper we explore a Bayesian hierarchical clustering algorithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy agglomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.
Digital Business Strategy and Value Creation: Framing the Dynamic Cycle of Control Points
(2013)

Within changing value networks, the profits and competitive advantages of participation reside dynamically at control points that are the positions of greatest value and/or power. The enterprises that hold these positions have a great deal of control over how the network operates, how the benefits are redistributed, and how this influences the execution of a digital business strategy. This article is based on a field study that provides preliminary, yet promising, empirical evidence that sheds light on the dynamic cycle of value creation and value capture points in digitally enabled networks in response to triggers related to technology and business strategy. We use the context of the European and U.S. broadcasting industry. Specifically, we illustrate how incremental innovations may shift value networks from static, vertically integrated networks to more loosely coupled networks, and how cross-boundary industry disruptions may then, in turn, shift those to two-sided markets. Based on our analysis we provide insights and implications for digital business strategy research and practice.
Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs
(2019)

We tackle Attitude Detection, which we define as the task of extracting the replier's attitude, i.e., a target-polarity pair, from a given one-round conversation. While previous studies considered Target Extraction and Polarity Classification separately, we regard them as subtasks of Attitude Detection. Our experimental results show that treating the two subtasks independently is not the optimal solution for Attitude Detection, as achieving high performance in each subtask is not sufficient for obtaining correct target-polarity pairs. Our jointly trained model AD-NET substantially outperforms the separately trained models by alleviating the target-polarity mismatch problem. Moreover, we proposed a method utilising the attitude detection model to improve retrieval-based chatbots by re-ranking the response candidates with attitude features. Human evaluation indicates that with attitude detection integrated, the new responses to the sampled queries from are statistically significantly more consistent, coherent, engaging and informative than the original ones obtained from a commercial chatbot.
Robust misinterpretation of confidence intervals.
(2014)

Null hypothesis significance testing (NHST) is undoubtedly the most common inferential technique used to justify claims in the social sciences. However, even staunch defenders of NHST agree that its outcomes are often misinterpreted. Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual. Nevertheless, little is known about how researchers interpret CIs. In this study, 120 researchers and 442 students-all in the field of psychology-were asked to assess the truth value of six particular statements involving different interpretations of a CI. Although all six statements were false, both researchers and students endorsed, on average, more than three statements, indicating a gross misunderstanding of CIs. Self-declared experience with statistics was not related to researchers' performance, and, even more surprisingly, researchers hardly outperformed the students, even though the students had not received any education on statistical inference whatsoever. Our findings suggest that many researchers do not know the correct interpretation of a CI. The misunderstandings surrounding p-values and CIs are particularly unfortunate because they constitute the main tools by which psychologists draw conclusions from data.
Low-complexity image denoising based on statistical modeling of wavelet coefficients
(1999)

We introduce a simple spatially adaptive statistical model for wavelet image coefficients and apply it to image denoising. Our model is inspired by a recent wavelet image compression algorithm, the estimation-quantization (EQ) coder. We model wavelet image coefficients as zero-mean Gaussian random variables with high local correlation. We assume a marginal prior distribution on wavelet coefficients variances and estimate them using an approximate maximum a posteriori probability rule. Then we apply an approximate minimum mean squared error estimation procedure to restore the noisy wavelet image coefficients. Despite the simplicity of our method, both in its concept and implementation, our denoising results are among the best reported in the literature.
Crowdlet: Optimal worker recruitment for self-organized mobile crowdsourcing
(2016)

In this paper, we advocate Crowdlet, a novel self-organized mobile crowdsourcing paradigm, in which a mobile task requester can proactively exploit a massive crowd of encountered mobile workers at real-time for quick and high-quality results. We present a comprehensive system model of Crowdlet that defines task, worker arrival and worker ability models. Further, we introduce a service quality concept to indicate the expected service gain that a requester can enjoy when he recruits an encountered worker, by jointly taking into account worker ability, real-timeness and task reward. Based on the models, we formulate an online worker recruitment problem to maximize the expected sum of service quality. We derive an optimal worker recruitment policy through the dynamic programming principle, and show that it exhibits a nice threshold based structure. We conduct extensive performance evaluation based on real traces, and numerical results demonstrate that our policy can achieve superior performance and improve more than 30% performance gain over classic policies. Besides, our Android prototype shows that Crowdlet is cost-efficient, requiring less than 7 seconds and 6 Joule in terms of time and energy cost for policy computation in most cases.
The population genetics of the Jewish people
(2012)

Adherents to the Jewish faith have resided in numerous geographic locations over the course of three millennia. Progressively more detailed population genetic analysis carried out independently by multiple research groups over the past two decades has revealed a pattern for the population genetic architecture of contemporary Jews descendant from globally dispersed Diaspora communities. This pattern is consistent with a major, but variable component of shared Near East ancestry, together with variable degrees of admixture and introgression from the corresponding host Diaspora populations. By combining analysis of monoallelic markers with recent genome-wide variation analysis of simple tandem repeats, copy number variations, and single-nucleotide polymorphisms at high density, it has been possible to determine the relative contribution of sex-specific migration and introgression to map founder events and to suggest demographic histories corresponding to western and eastern Diaspora migrations, as well as subsequent microevolutionary events. These patterns have been congruous with the inferences of many, but not of all historians using more traditional tools such as archeology, archival records, linguistics, comparative analysis of religious narrative, liturgy and practices. Importantly, the population genetic architecture of Jews helps to explain the observed patterns of health and disease-relevant mutations and phenotypes which continue to be carefully studied and catalogued, and represent an important resource for human medical genetics research. The current review attempts to provide a succinct update of the more recent developments in a historical and human health context.
Learning deep physiological models of affect
(2013)

More than 15 years after the early studies in Affective Computing (AC), [1] the problem of detecting and modeling emotions in the context of human-computer interaction (HCI) remains complex and largely unexplored. The detection and modeling of emotion is, primarily, the study and use of artificial intelligence (AI) techniques for the construction of computational models of emotion. The key challenges one faces when attempting to model emotion [2] are inherent in the vague definitions and fuzzy boundaries of emotion, and in the modeling methodology followed. In this context, open research questions are still present in all key components of the modeling process. These include, first, the appropriateness of the modeling tool employed to map emotional manifestations and responses to annotated affective states; second, the processing of signals that express these manifestations (i.e., model input); and third, the way affective annotation (i.e., model output) is handled. This paper touches upon all three key components of an affective model (i.e., input, model, output) and introduces the use of deep learning (DL) [3], [4], [5] methodologies for affective modeling from multiple physiological signals.
Three Implementations of SquishQL, a Simple RDF Query Language
(2002)

RDF provides a basic way to represent data for the Semantic Web. We have been experimenting with the query paradigm for working with RDF data in semantic web applications. Query of RDF data provides a declarative access mechanism that is suitable for application usage and remote access. We describe work on a conceptual model for querying RDF data that refines ideas first presented in at the W3C workshop on Query Languages [14] and the design of one possible syntax, derived from [7], that is suitable for application programmers. Further, we present experience gained in three implementations of the query language.
Social Media: The Good, the Bad, and the Ugly
(2016)

Companies when strategizing are looking for innovative ways to have a competitive advantage over their competitors. One way in which they compete is by the adoption of social media. Social media has evolved over the years and as a result, new concepts and applications are developed which promises to provide business value to a company. However, despite the usefulness of social media, many businesses fail to reap its full benefits. The current literature shows evidence of lack of strategically designed process for companies to successfully implement social media. The purpose of this study is to suggest a framework which provides the necessary alignment between social media goals with business objectives. From the literature review, a social media strategy framework was derived to offer an effective step by step approach to the development and imple‐ mentation of social media goals aligned with a company’s business objectives. The contribution to this study is the development of a social media strategy framework that can be used by organisations for business value.
Web Mining for Information Retrieval
(2018)

The present era is engulfed in data and it is quite difficult to churn the emergence of unstructured data in order to mine relevant information. In this paper we present a basic outline of web mining techniques which changed the scenario of today world and help in retrieval of information. Web Mining actually referred as mining of interesting pattern by using set of tools and techniques from the vast pool of web. It focuses on different aspects of web mining referred as Web Content Mining, Web Structure Mining and Web Usage Mining. The overview of these mining techniques which help in retrieving desired information are covered along with the approaches used and algorithm employed.
Argumentation Mining: Where are we now, where do we want to be and how do we get there?
(2013)

This paper gives a short overview of the state-of-the-art and goals of argumentation mining and it provides ideas for further research. Its content is based on two invited lectures on argumentation mining respectively at the FIRE 2013 conference at the India International Center in New Delhi, India and a lecture given as SICSA distinguished visitor at the University of Dundee, UK in the summer of 2014.
1 Unsupervised Feature Learning in Time Series 2 Prediction Using Continuous Deep Belief Network 3
(2018)

A continuous Deep Belief Network (cDBN) with two hidden layers is proposed in this 18 paper, focusing on the problem of weak feature learning ability when dealing with continuous data. 19 In cDBN, the input data is trained in an unsupervised way by using continuous version of transfer 20 functions, the contrastive divergence is designed in hidden layer training process to raise 21 convergence speed, an improved dropout strategy is then implemented in unsupervised training to 22 realize features learning by de-cooperating between the units, and then the network is fine-tuned 23 using back propagation algorithm. Besides, hyper-parameters are analysed through stability 24 analysis to assure the network can find the optimal. Finally, the experiments on Lorenz chaos series, 25 CATS benchmark and other real world like CO2 and waste water parameters forecasting show that 26 cDBN has the advantage of higher accuracy, simpler structure and faster convergence speed than 27 other methods. 28
Influence of Some Parameters on the Effectiveness of Induction Heating
(2008)

We have investigated the effectiveness of heating conductive plates by induction in low frequencies analytically and numerically, in relation to the shape of the plate, the area of the region exposed to the magnetic flux, and the position of the exposed region with respect to the center of the plate. We considered both uniform and nonuniform magnetic fields. For plates with equivalent area exposed to the same uniform magnetic flux, the one with the most symmetrical shape is heated most effectively. If a coil is employed for the excitation, the results depend on the shape of the plate, the shape of the coil section, and the coil lift-off. When only the central region of a plate is exposed to a variable magnetic flux, there is a specific value of the exposed area for which the power dissipated in the plate material reaches a maximum. The most effective heating of a plate partially exposed occurs when the axis of the exciting coil is at the plate center.
A Model of a Localized Cross-Border E-Commerce
(2013)

By the explosive growth of B2B e-commerce transactions in international supply chains and the rapid increase of business documents in Iran’s cross-border trading, effective management of trade processes over borders is vital in B2B e-commerce systems. Structure of the localized model in this paper is based on three major layers of a B2B e-commerce infrastructure, which are messaging layer, business process layer and content layer. For each of these layers proper standards and solutions are chosen due to Iran’s e-commerce requirements. As it is needed to move smoothly towards electronic documents in Iran, UNedocs standard is suggested to support the contents of both paper and electronic documents. The verification of the suggested model is done by presenting a four phase scenario through case study method. The localized model in this paper tries to make a strategic view of business documents exchange in trade processes, and getting closer to the key target of regional single windows establishment in global trade e-supply chains.
Object modelling by registration of multiple range images
(1992)

We study the problem of creating a complete model of a physical object. Although this may be possible using intensity images, we use here range images which directly provide access t o three dimensional information. T h e first problem that we need t o solve is t o find the transformation between the different views. Previous approaches have either assumed this transformation t o be known (which is extremely difficult for a complete model), or computed it with feature matching (which is not accurate enough for integration). In this paper, we propose a new approach which works on range d a t a directly, and registers successive views with enough overlapping area t o get an accurate transformation between views. This is performed by minimizing afunctional which does not require point t o point matches. We give the details of the registration method and modeling procedure, and illustrate them on real range images of complex objects. 1 Introduction Creating models of physical objects is a necessary component machine of biological vision modules. Such models can then be used in object recognition, pose estimation or inspection tasks. If the object of interest has been precisely designed, then such a model exists in the form of a CAD model. In many applications, however, it is either not possible or not practical to have access to such CAD models, and we need to build models from the physical object. Some researchers bypass the problem by using a model which consist,s of multiple views ([4], [a]), but t,liis is not, always enough. If one needs a complete model of an object, the following steps are necessary: 1. data acquisition, 2. registration between views, 3. integration of views. By view we mean the 3D surface information of the object from specific point of view. While the integration process is very dependent on the representation scheme used, the precondition for performing integration consists of knowing the transformation between the data from different views. The goal of registrat,ion is to find such a transformat.ion, which is also known as t,he covrespon,den,ce problem. This problem has been a t the core of many previous research efforts: Bhanu [a] developed an object modeling system for object recognition by rotating object through known angles to acquire multiple views. Chien et al. [3] and Ahuja and Veen-stra [l] used orthogonal views to construct octree object ' models. With these methods, …
Rigid 3D geometry matching for grasping of known objects in cluttered scenes
(2012)

In this paper, we present an efficient 3D object recognition and pose estimation approach for grasping procedures in cluttered and occluded environments. In contrast to common appearance-based approaches, we rely solely on 3D geometry information. Our method is based on a robust geometric descriptor, a hashing technique and an efficient, localized RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method simultaneously recognizes multiple model instances and estimates their pose in the scene. A variety of tests shows that the proposed method performs well on noisy, cluttered and unsegmented range scans in which only small parts of the objects are visible. The main procedure of the algorithm has a linear time complexity resulting in a high recognition speed which allows a direct integration of the method into a continuous manipulation task. The experimental validation with a 7-degrees-of-freedom Cartesian impedance controlled robot shows how the method can be used for grasping objects from a complex random stack. This application demonstrates how the integration of computer vision and softrobotics leads to a robotic system capable of acting in unstructured and occluded environments.
Combining Scale-Space and Similarity-Based Aspect Graphs for Fast 3D Object Recognition
(2012)

This paper describes an approach for recognizing instances of a 3D object in a single camera image and for determining their 3D poses. A hierarchical model is generated solely based on the geometry information of a 3D CAD model of the object. The approach does not rely on texture or reflectance information of the object's surface, making it useful for a wide range of industrial and robotic applications, e.g., bin-picking. A hierarchical view-based approach that addresses typical problems of previous methods is applied: It handles true perspective, is robust to noise, occlusions, and clutter to an extent that is sufficient for many practical applications, and is invariant to contrast changes. For the generation of this hierarchical model, a new model image generation technique by which scale-space effects can be taken into account is presented. The necessary object views are derived using a similarity-based aspect graph. The high robustness of an exhaustive search is combined with an efficient hierarchical search. The 3D pose is refined by using a least-squares adjustment that minimizes geometric distances in the image, yielding a position accuracy of up to 0.12 percent with respect to the object distance, and an orientation accuracy of up to 0.35 degree in our tests. The recognition time is largely independent of the complexity of the object, but depends mainly on the range of poses within which the object may appear in front of the camera. For efficiency reasons, the approach allows the restriction of the pose range depending on the application. Typical runtimes are in the range of a few hundred ms.